Many critical systems rely on the persistent accumulation of image data. However, these critical systems lack fail safes to monitor data streams of images in order to ensure that the incoming data streams are not corrupted or otherwise perturbed. That is, incoming data streams may change. This may be due to a change in the input or a problem with the input source. The systems in place to review, analyze, and classify the images contained in the data streams will continue to work; however, the classifications will be wrong. This problem of detecting corrupted, perturbed, or changing images is further complicated by the unstructured nature of images. In the context of banking, depositing checks at automated teller machines (ATMs) poses a unique problem due to the non-standardized nature of checks. In this regard, checks come in unique shapes and sizes and with various ornamental designs. New formats of checks cause system failures due to the systems inability to process the new formats. Likewise, varying camera systems may change the way the image of the check is digitized. Accordingly, there is a problem with existing systems detecting when input images deviate from the expected and notifying administrators of such deviations. Furthermore, it can be difficult to differentiate between a single anomalous input and a fundamental shift or change in the stream itself.
Aspects described herein may address these and other problems, and generally improve the quality, efficiency, and speed with which systems detect deviations in input image streams.